Overview

Dataset statistics

Number of variables12
Number of observations1279
Missing cells0
Missing cells (%)0.0%
Duplicate rows159
Duplicate rows (%)12.4%
Total size in memory120.0 KiB
Average record size in memory96.1 B

Variable types

Numeric12

Alerts

Dataset has 159 (12.4%) duplicate rowsDuplicates
fixed acidity is highly overall correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly overall correlated with citric acidHigh correlation
citric acid is highly overall correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxideHigh correlation
density is highly overall correlated with fixed acidityHigh correlation
pH is highly overall correlated with fixed acidity and 1 other fieldsHigh correlation
citric acid has 105 (8.2%) zerosZeros

Reproduction

Analysis started2023-04-16 15:50:41.992314
Analysis finished2023-04-16 15:51:09.130320
Duration27.14 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct91
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3236904
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:09.370331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.19
Q17.1
median7.9
Q39.2
95-th percentile11.61
Maximum15.9
Range11.3
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.7242431
Coefficient of variation (CV)0.20714887
Kurtosis1.0970287
Mean8.3236904
Median Absolute Deviation (MAD)1
Skewness0.96450626
Sum10646
Variance2.9730142
MonotonicityNot monotonic
2023-04-16T11:51:09.689799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 48
 
3.8%
7.1 48
 
3.8%
7.8 45
 
3.5%
7.7 43
 
3.4%
7.5 43
 
3.4%
7 39
 
3.0%
7.4 38
 
3.0%
7.6 37
 
2.9%
8.2 36
 
2.8%
6.9 35
 
2.7%
Other values (81) 867
67.8%
ValueCountFrequency (%)
4.6 1
 
0.1%
4.7 1
 
0.1%
4.9 1
 
0.1%
5 4
 
0.3%
5.1 3
 
0.2%
5.2 6
0.5%
5.3 4
 
0.3%
5.4 3
 
0.2%
5.6 12
0.9%
5.7 1
 
0.1%
ValueCountFrequency (%)
15.9 1
 
0.1%
15.6 2
0.2%
15.5 1
 
0.1%
15 1
 
0.1%
13.8 1
 
0.1%
13.7 2
0.2%
13.4 1
 
0.1%
13.3 3
0.2%
13.2 3
0.2%
13 3
0.2%

volatile acidity
Real number (ℝ)

Distinct138
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53055903
Minimum0.12
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:09.901389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.28
Q10.4
median0.52
Q30.64
95-th percentile0.8405
Maximum1.58
Range1.46
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.17927524
Coefficient of variation (CV)0.33789877
Kurtosis1.5071039
Mean0.53055903
Median Absolute Deviation (MAD)0.12
Skewness0.74472878
Sum678.585
Variance0.032139613
MonotonicityNot monotonic
2023-04-16T11:51:10.091335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.43 38
 
3.0%
0.5 37
 
2.9%
0.59 36
 
2.8%
0.6 33
 
2.6%
0.58 32
 
2.5%
0.39 30
 
2.3%
0.41 30
 
2.3%
0.4 30
 
2.3%
0.36 29
 
2.3%
0.52 29
 
2.3%
Other values (128) 955
74.7%
ValueCountFrequency (%)
0.12 2
 
0.2%
0.16 2
 
0.2%
0.18 8
0.6%
0.19 1
 
0.1%
0.2 3
 
0.2%
0.21 5
0.4%
0.22 4
0.3%
0.23 4
0.3%
0.24 7
0.5%
0.25 6
0.5%
ValueCountFrequency (%)
1.58 1
 
0.1%
1.33 2
0.2%
1.24 1
 
0.1%
1.185 1
 
0.1%
1.18 1
 
0.1%
1.13 1
 
0.1%
1.07 1
 
0.1%
1.04 3
0.2%
1.035 1
 
0.1%
1.025 1
 
0.1%

citric acid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27247068
Minimum0
Maximum1
Zeros105
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:10.564039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.26
Q30.43
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.19544779
Coefficient of variation (CV)0.71731677
Kurtosis-0.78099705
Mean0.27247068
Median Absolute Deviation (MAD)0.17
Skewness0.3139054
Sum348.49
Variance0.038199838
MonotonicityNot monotonic
2023-04-16T11:51:10.795149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 105
 
8.2%
0.49 55
 
4.3%
0.02 39
 
3.0%
0.24 38
 
3.0%
0.26 32
 
2.5%
0.01 30
 
2.3%
0.32 28
 
2.2%
0.21 28
 
2.2%
0.1 28
 
2.2%
0.03 26
 
2.0%
Other values (69) 870
68.0%
ValueCountFrequency (%)
0 105
8.2%
0.01 30
 
2.3%
0.02 39
 
3.0%
0.03 26
 
2.0%
0.04 21
 
1.6%
0.05 15
 
1.2%
0.06 16
 
1.3%
0.07 18
 
1.4%
0.08 22
 
1.7%
0.09 25
 
2.0%
ValueCountFrequency (%)
1 1
 
0.1%
0.78 1
 
0.1%
0.76 3
0.2%
0.75 1
 
0.1%
0.74 3
0.2%
0.73 3
0.2%
0.72 1
 
0.1%
0.71 1
 
0.1%
0.7 2
0.2%
0.69 2
0.2%

residual sugar
Real number (ℝ)

Distinct82
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.555473
Minimum0.9
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:11.239230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.5
Q11.9
median2.2
Q32.6
95-th percentile5.2
Maximum15.5
Range14.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.4357897
Coefficient of variation (CV)0.56184888
Kurtosis29.03581
Mean2.555473
Median Absolute Deviation (MAD)0.3
Skewness4.5645293
Sum3268.45
Variance2.0614919
MonotonicityNot monotonic
2023-04-16T11:51:11.421404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 119
 
9.3%
2.2 108
 
8.4%
2.1 105
 
8.2%
1.8 100
 
7.8%
1.9 91
 
7.1%
2.3 86
 
6.7%
2.4 70
 
5.5%
2.5 68
 
5.3%
2.6 64
 
5.0%
1.7 61
 
4.8%
Other values (72) 407
31.8%
ValueCountFrequency (%)
0.9 2
 
0.2%
1.2 5
 
0.4%
1.3 5
 
0.4%
1.4 27
 
2.1%
1.5 28
 
2.2%
1.6 43
3.4%
1.65 1
 
0.1%
1.7 61
4.8%
1.75 2
 
0.2%
1.8 100
7.8%
ValueCountFrequency (%)
15.5 1
0.1%
15.4 2
0.2%
13.9 1
0.1%
13.8 2
0.2%
12.9 1
0.1%
11 1
0.1%
10.7 1
0.1%
9 1
0.1%
8.9 1
0.1%
8.8 2
0.2%

chlorides
Real number (ℝ)

Distinct143
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.088448006
Minimum0.012
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:11.550240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.054
Q10.071
median0.08
Q30.091
95-th percentile0.132
Maximum0.611
Range0.599
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.049332416
Coefficient of variation (CV)0.55775611
Kurtosis39.524486
Mean0.088448006
Median Absolute Deviation (MAD)0.01
Skewness5.5714775
Sum113.125
Variance0.0024336872
MonotonicityNot monotonic
2023-04-16T11:51:11.670429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 57
 
4.5%
0.074 41
 
3.2%
0.076 39
 
3.0%
0.077 38
 
3.0%
0.079 37
 
2.9%
0.078 37
 
2.9%
0.082 37
 
2.9%
0.084 37
 
2.9%
0.075 37
 
2.9%
0.071 36
 
2.8%
Other values (133) 883
69.0%
ValueCountFrequency (%)
0.012 2
0.2%
0.034 1
 
0.1%
0.038 2
0.2%
0.039 3
0.2%
0.041 2
0.2%
0.042 3
0.2%
0.043 1
 
0.1%
0.044 2
0.2%
0.045 4
0.3%
0.046 3
0.2%
ValueCountFrequency (%)
0.611 1
 
0.1%
0.61 1
 
0.1%
0.467 1
 
0.1%
0.464 1
 
0.1%
0.415 3
0.2%
0.414 2
0.2%
0.403 1
 
0.1%
0.401 1
 
0.1%
0.387 1
 
0.1%
0.369 1
 
0.1%

free sulfur dioxide
Real number (ℝ)

Distinct57
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.876075
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:11.964106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median14
Q321
95-th percentile35
Maximum68
Range67
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.313517
Coefficient of variation (CV)0.64962636
Kurtosis1.8279779
Mean15.876075
Median Absolute Deviation (MAD)7
Skewness1.1988345
Sum20305.5
Variance106.36863
MonotonicityNot monotonic
2023-04-16T11:51:12.273328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 112
 
8.8%
5 86
 
6.7%
15 62
 
4.8%
12 62
 
4.8%
10 62
 
4.8%
7 60
 
4.7%
17 55
 
4.3%
16 48
 
3.8%
9 47
 
3.7%
13 44
 
3.4%
Other values (47) 641
50.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 1
 
0.1%
3 41
 
3.2%
4 29
 
2.3%
5 86
6.7%
5.5 1
 
0.1%
6 112
8.8%
7 60
4.7%
8 38
 
3.0%
9 47
3.7%
ValueCountFrequency (%)
68 2
0.2%
66 1
 
0.1%
57 1
 
0.1%
55 2
0.2%
53 1
 
0.1%
52 2
0.2%
51 1
 
0.1%
50 2
0.2%
48 3
0.2%
47 1
 
0.1%

total sulfur dioxide
Real number (ℝ)

Distinct139
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.657154
Minimum6
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:12.645048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q122
median38
Q363
95-th percentile113.1
Maximum289
Range283
Interquartile range (IQR)41

Descriptive statistics

Standard deviation32.941962
Coefficient of variation (CV)0.70604311
Kurtosis2.7716785
Mean46.657154
Median Absolute Deviation (MAD)18
Skewness1.3853179
Sum59674.5
Variance1085.1729
MonotonicityNot monotonic
2023-04-16T11:51:13.101417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 35
 
2.7%
20 31
 
2.4%
18 30
 
2.3%
14 29
 
2.3%
23 29
 
2.3%
38 27
 
2.1%
24 27
 
2.1%
13 25
 
2.0%
15 24
 
1.9%
31 23
 
1.8%
Other values (129) 999
78.1%
ValueCountFrequency (%)
6 3
 
0.2%
7 3
 
0.2%
8 12
0.9%
9 12
0.9%
10 23
1.8%
11 18
1.4%
12 22
1.7%
13 25
2.0%
14 29
2.3%
15 24
1.9%
ValueCountFrequency (%)
289 1
 
0.1%
165 1
 
0.1%
155 1
 
0.1%
152 1
 
0.1%
151 2
0.2%
149 1
 
0.1%
147 2
0.2%
145 3
0.2%
144 3
0.2%
143 2
0.2%

density
Real number (ℝ)

Distinct386
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99677398
Minimum0.99007
Maximum1.00369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:13.374393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.99007
5-th percentile0.9937
Q10.995655
median0.9968
Q30.997845
95-th percentile0.9998
Maximum1.00369
Range0.01362
Interquartile range (IQR)0.00219

Descriptive statistics

Standard deviation0.0018559414
Coefficient of variation (CV)0.0018619481
Kurtosis1.0892548
Mean0.99677398
Median Absolute Deviation (MAD)0.0011
Skewness0.055801352
Sum1274.8739
Variance3.4445187 × 10-6
MonotonicityNot monotonic
2023-04-16T11:51:13.574775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9968 30
 
2.3%
0.9972 28
 
2.2%
0.9976 27
 
2.1%
0.9978 26
 
2.0%
0.998 23
 
1.8%
0.997 20
 
1.6%
0.9994 19
 
1.5%
0.9964 19
 
1.5%
0.9988 18
 
1.4%
0.9982 18
 
1.4%
Other values (376) 1051
82.2%
ValueCountFrequency (%)
0.99007 2
0.2%
0.9902 1
0.1%
0.99064 2
0.2%
0.9912 1
0.1%
0.9915 1
0.1%
0.99154 1
0.1%
0.99157 1
0.1%
0.9916 2
0.2%
0.99182 2
0.2%
0.9921 1
0.1%
ValueCountFrequency (%)
1.00369 2
0.2%
1.0032 1
 
0.1%
1.00315 2
0.2%
1.00289 1
 
0.1%
1.0026 2
0.2%
1.00242 2
0.2%
1.0022 1
 
0.1%
1.0021 2
0.2%
1.0015 1
 
0.1%
1.0014 3
0.2%

pH
Real number (ℝ)

Distinct86
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3116497
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:13.925321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3.07
Q13.21
median3.31
Q33.4
95-th percentile3.57
Maximum4.01
Range1.27
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.15401568
Coefficient of variation (CV)0.046507236
Kurtosis0.72037991
Mean3.3116497
Median Absolute Deviation (MAD)0.1
Skewness0.19778842
Sum4235.6
Variance0.023720829
MonotonicityNot monotonic
2023-04-16T11:51:14.131307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 50
 
3.9%
3.26 45
 
3.5%
3.36 43
 
3.4%
3.38 40
 
3.1%
3.29 38
 
3.0%
3.39 38
 
3.0%
3.28 35
 
2.7%
3.32 35
 
2.7%
3.16 32
 
2.5%
3.22 32
 
2.5%
Other values (76) 891
69.7%
ValueCountFrequency (%)
2.74 1
 
0.1%
2.88 2
0.2%
2.89 3
0.2%
2.9 1
 
0.1%
2.92 3
0.2%
2.93 3
0.2%
2.94 4
0.3%
2.95 1
 
0.1%
2.98 3
0.2%
2.99 2
0.2%
ValueCountFrequency (%)
4.01 1
 
0.1%
3.9 2
 
0.2%
3.85 1
 
0.1%
3.78 2
 
0.2%
3.75 1
 
0.1%
3.74 1
 
0.1%
3.72 1
 
0.1%
3.71 4
0.3%
3.69 4
0.3%
3.68 5
0.4%

sulphates
Real number (ℝ)

Distinct92
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66002346
Minimum0.37
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:14.616219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile0.47
Q10.55
median0.62
Q30.73
95-th percentile0.931
Maximum2
Range1.63
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.17460463
Coefficient of variation (CV)0.26454306
Kurtosis12.614917
Mean0.66002346
Median Absolute Deviation (MAD)0.08
Skewness2.5919993
Sum844.17
Variance0.030486776
MonotonicityNot monotonic
2023-04-16T11:51:14.918474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 56
 
4.4%
0.58 53
 
4.1%
0.62 53
 
4.1%
0.54 50
 
3.9%
0.56 46
 
3.6%
0.55 43
 
3.4%
0.57 43
 
3.4%
0.64 41
 
3.2%
0.63 38
 
3.0%
0.53 37
 
2.9%
Other values (82) 819
64.0%
ValueCountFrequency (%)
0.37 1
 
0.1%
0.39 4
 
0.3%
0.4 3
 
0.2%
0.42 4
 
0.3%
0.43 7
 
0.5%
0.44 13
1.0%
0.45 6
 
0.5%
0.46 17
1.3%
0.47 16
1.3%
0.48 27
2.1%
ValueCountFrequency (%)
2 1
0.1%
1.98 1
0.1%
1.95 2
0.2%
1.62 1
0.1%
1.61 1
0.1%
1.59 1
0.1%
1.56 1
0.1%
1.36 2
0.2%
1.34 1
0.1%
1.33 1
0.1%

alcohol
Real number (ℝ)

Distinct60
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.4181
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:15.341696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311.1
95-th percentile12.5
Maximum14.9
Range6.5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.0526943
Coefficient of variation (CV)0.10104475
Kurtosis0.24205079
Mean10.4181
Median Absolute Deviation (MAD)0.7
Skewness0.86568773
Sum13324.75
Variance1.1081652
MonotonicityNot monotonic
2023-04-16T11:51:15.818433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 112
 
8.8%
9.4 81
 
6.3%
9.8 64
 
5.0%
10.5 59
 
4.6%
9.2 56
 
4.4%
10 52
 
4.1%
9.6 48
 
3.8%
9.3 47
 
3.7%
11 47
 
3.7%
10.9 43
 
3.4%
Other values (50) 670
52.4%
ValueCountFrequency (%)
8.4 1
 
0.1%
8.8 2
 
0.2%
9 25
 
2.0%
9.05 1
 
0.1%
9.1 20
 
1.6%
9.2 56
4.4%
9.233333333 1
 
0.1%
9.25 1
 
0.1%
9.3 47
3.7%
9.4 81
6.3%
ValueCountFrequency (%)
14.9 1
 
0.1%
14 4
0.3%
13.6 4
0.3%
13.56666667 1
 
0.1%
13.5 1
 
0.1%
13.4 3
0.2%
13.3 3
0.2%
13.1 1
 
0.1%
13 3
0.2%
12.9 6
0.5%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6239249
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-16T11:51:15.980117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80690869
Coefficient of variation (CV)0.14347786
Kurtosis0.32462002
Mean5.6239249
Median Absolute Deviation (MAD)1
Skewness0.19367088
Sum7193
Variance0.65110164
MonotonicityNot monotonic
2023-04-16T11:51:16.208451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 551
43.1%
6 506
39.6%
7 157
 
12.3%
4 43
 
3.4%
8 13
 
1.0%
3 9
 
0.7%
ValueCountFrequency (%)
3 9
 
0.7%
4 43
 
3.4%
5 551
43.1%
6 506
39.6%
7 157
 
12.3%
8 13
 
1.0%
ValueCountFrequency (%)
8 13
 
1.0%
7 157
 
12.3%
6 506
39.6%
5 551
43.1%
4 43
 
3.4%
3 9
 
0.7%

Interactions

2023-04-16T11:51:05.384071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.139006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.825082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.684739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.358715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.129388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.801847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:48.637475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:52.226671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:55.798861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:58.758591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:02.160353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:05.605497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.196196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.884335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.757752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.411698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.182107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.856675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:48.894098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:52.381133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:56.041307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:59.082066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:02.437381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:05.932341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.274416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.942579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.815236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.469491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.239763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.916384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:49.203781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:52.652451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:56.415117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:59.323536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:02.759254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:06.180636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.326455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.010952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.868337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.524030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.295171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:46.117123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:49.418759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:52.914125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:56.642217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:59.437034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:03.111857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:06.300036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.379910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.067865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.921862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.594366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.357535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:46.232274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:49.764745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:53.237083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:56.837035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:59.876706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:03.229413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:06.428059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.432314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.135229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.975769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.649618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.411032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:46.435164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:49.996920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:53.395492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:57.165404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:00.108423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:03.556057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:06.719977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.488449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.234067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.030122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.707704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.467752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:46.706823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:50.229168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:53.753994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:57.506422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:00.436147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:03.812362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:07.131185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.550529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.377599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.083204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.847403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.525224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:47.340029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:50.407723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:54.276528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:57.731308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:01.063619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:04.021043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:07.487426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.607445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.442059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.145124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.904087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.581616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:47.635369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:50.658075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:54.623598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:57.862282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:01.412223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:04.363309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:07.676548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.662277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.501029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.197987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.959813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.635420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:47.890276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:51.014077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:54.917048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:58.014121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:01.530000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:04.638558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:08.047035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.718363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.566224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.255146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.023983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.695499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:48.290357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:51.427638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:55.267219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:58.279655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:01.688001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:04.893528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:08.353368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:42.770187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:43.620372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:44.306798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.077132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:45.750529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:48.448063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:51.840104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:55.556631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:50:58.506175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:01.993674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:05.249498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-16T11:51:16.513196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
fixed acidity1.000-0.2620.6590.2040.250-0.175-0.0890.616-0.7030.205-0.0680.101
volatile acidity-0.2621.000-0.6050.0370.1710.0320.1060.0290.217-0.342-0.233-0.369
citric acid0.659-0.6051.0000.1750.124-0.0800.0100.360-0.5450.3330.0910.201
residual sugar0.2040.0370.1751.0000.1900.0550.1250.431-0.0650.0350.1310.033
chlorides0.2500.1710.1240.1901.000-0.0050.1200.395-0.2430.006-0.278-0.181
free sulfur dioxide-0.1750.032-0.0800.055-0.0051.0000.797-0.0330.1160.053-0.092-0.058
total sulfur dioxide-0.0890.1060.0100.1250.1200.7971.0000.131-0.0150.001-0.270-0.204
density0.6160.0290.3600.4310.395-0.0330.1311.000-0.3080.148-0.445-0.173
pH-0.7030.217-0.545-0.065-0.2430.116-0.015-0.3081.000-0.0810.195-0.027
sulphates0.205-0.3420.3330.0350.0060.0530.0010.148-0.0811.0000.2250.385
alcohol-0.068-0.2330.0910.131-0.278-0.092-0.270-0.4450.1950.2251.0000.480
quality0.101-0.3690.2010.033-0.181-0.058-0.204-0.173-0.0270.3850.4801.000

Missing values

2023-04-16T11:51:08.602416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-16T11:51:08.878354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
08.70.6900.313.00.08623.081.01.000203.480.7411.66
16.10.2100.401.40.06640.5165.00.991203.250.5911.96
210.90.3900.471.80.1186.014.00.998203.300.759.86
38.80.6850.261.60.08816.023.00.996943.320.479.45
48.41.0350.156.00.07311.054.00.999003.370.499.95
58.20.3900.492.30.09947.0133.00.997903.380.999.85
66.30.5500.151.80.07726.035.00.993143.320.8211.66
78.00.5000.392.60.08212.046.00.998503.430.6210.76
88.20.6400.272.00.0955.077.00.997473.130.629.16
911.50.1800.514.00.1044.023.00.999603.280.9710.16
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
12698.10.7300.002.50.08112.024.00.997983.380.469.64
127010.30.5000.422.00.06921.051.00.998203.160.7211.56
12718.80.5500.042.20.11914.056.00.996203.210.6010.96
12726.40.3900.333.30.04612.053.00.992943.360.6212.26
12739.40.4000.472.50.0876.020.00.997723.150.5010.55
12749.10.6000.001.90.0585.010.00.997703.180.6310.46
12758.20.6350.102.10.07325.060.00.996383.290.7510.96
12767.20.6200.062.70.07715.085.00.997463.510.549.55
12777.90.2000.351.70.0547.015.00.994583.320.8011.97
12785.80.2900.261.70.0633.011.00.991503.390.5413.56

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
567.50.5100.021.70.08413.031.00.995383.360.5410.564
146.70.4600.241.70.07718.034.00.994803.390.6010.663
277.00.6900.072.50.09115.021.00.995723.380.6011.363
417.20.6300.001.90.09714.038.00.996753.370.589.063
437.20.6950.132.00.07612.020.00.995463.290.5410.153
958.30.6500.102.90.08917.040.00.998033.290.559.553
1189.30.3600.391.50.08041.055.00.996523.470.7310.963
1289.90.5400.452.30.07116.040.00.999103.390.629.453
05.20.3400.001.80.05027.063.00.991603.680.7914.062
15.60.5000.092.30.04917.099.00.993703.630.6313.052